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                    神经网络
            
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                    支持向量机
            
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                    贝叶斯分类器
            
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                    集成学习
            
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                    聚类
            
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                    降维与度量学习
            
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                    特征选择与稀疏学习
            
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                    计算学习理论
            
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                    半监督学习
            
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                    概率图模型
            
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<p>&#x8F6C;&#x8F7D;&#x8BF7;&#x6CE8;&#x660E;&#x4F5C;&#x8005;&#xFF1A;<a href="https://github.com/ahangchen" target="_blank">&#x68A6;&#x91CC;&#x8336;</a></p>
<blockquote>
<p>Single Shot MultiBox Detector</p>
</blockquote>
<h2 id="introduction">Introduction</h2>
<p>&#x4E00;&#x53E5;&#x8BDD;&#x6982;&#x62EC;&#xFF1A;SSD&#x5C31;&#x662F;&#x5173;&#x4E8E;&#x7C7B;&#x522B;&#x7684;&#x591A;&#x5C3A;&#x5EA6;RPN&#x7F51;&#x7EDC;</p>
<p>&#x57FA;&#x672C;&#x601D;&#x8DEF;&#xFF1A;</p>
<ul>
<li>&#x57FA;&#x7840;&#x7F51;&#x7EDC;&#x540E;&#x63A5;&#x591A;&#x5C42;feature map</li>
<li>&#x591A;&#x5C42;feature map&#x5206;&#x522B;&#x5BF9;&#x5E94;&#x4E0D;&#x540C;&#x5C3A;&#x5EA6;&#x7684;&#x56FA;&#x5B9A;anchor</li>
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<p><img src="ssd_0.png" alt=""></p>
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<li>&#x8F93;&#x5165;&#xFF1A;300x300</li>
<li>&#x7ECF;&#x8FC7;VGG-16&#xFF08;&#x53EA;&#x5230;conv4_3&#x8FD9;&#x4E00;&#x5C42;&#xFF09;</li>
<li>&#x7ECF;&#x8FC7;&#x51E0;&#x5C42;&#x5377;&#x79EF;&#xFF0C;&#x5F97;&#x5230;&#x591A;&#x5C42;&#x5C3A;&#x5BF8;&#x9010;&#x6E10;&#x51CF;&#x5C0F;&#x7684;feature map</li>
<li><p>&#x6BCF;&#x5C42;feature map&#x5206;&#x522B;&#x505A;3x3&#x5377;&#x79EF;&#xFF0C;&#x6BCF;&#x4E2A;feature map cell(&#x53C8;&#x79F0;slide window)&#x5BF9;&#x5E94;k&#x4E2A;&#x7C7B;&#x522B;&#x548C;4&#x4E2A;bounding box offset&#xFF0C;&#x540C;&#x65F6;&#x5BF9;&#x5E94;&#x539F;&#x56FE;&#x4E2D;6&#xFF08;&#x6216;4&#xFF09;&#x4E2A;anchor(&#x53C8;&#x79F0;default box)</p>
<ul>
<li>38x38, &#x6700;&#x540E;3x3, 1x1&#x4E09;&#x4E2A;feature map&#x7684;&#x6BCF;&#x4E2A;feature map cell&#x53EA;&#x5BF9;&#x5E94;4&#x4E2A;anchor&#xFF0C;&#x5206;&#x522B;&#x4E3A;&#x5BBD;&#x9AD8;&#x6BD4;: 1:1&#x4E24;&#x79CD;&#xFF0C;1:2, 2:1&#x4E24;&#x79CD;&#xFF0C;&#x56E0;&#x6B64;&#x603B;&#x5171;&#x6709; 38 x 38 x 4 + 19 x 19 x 6 + 10 x 10 x 6 + 5 x 5 x 6 + 3 x 3 x 4 + 1 x 1 x 4 = 8732 &#x4E2A;anchor</li>
<li>&#x5176;&#x4ED6;feature map&#x7684;feature map cell&#x5BF9;&#x5E94;6&#x4E2A;anchor&#xFF0C;&#x5206;&#x522B;&#x4E3A;&#x5BBD;&#x9AD8;&#x6BD4;: 1:1&#x4E24;&#x79CD;&#xFF0C;1:2, 2:1&#x4E24;&#x79CD;&#xFF0C;1:3&#xFF0C; 3:1&#x4E24;&#x79CD;</li>
<li><p>&#x6BCF;&#x5C42;&#x7684;feature map cell&#x5BF9;&#x5E94;&#x7684;anchor&#x8BA1;&#x7B97;&#x65B9;&#x6CD5;&#x5982;&#x4E0B;</p>
<ul>
<li>&#x4F4D;&#x7F6E;&#xFF1A;&#x5047;&#x8BBE;&#x5F53;&#x524D;feature map cell&#x662F;&#x4F4D;&#x4E8E;&#x7B2C;i&#x884C;&#xFF0C;&#x7B2C;j&#x5217;&#xFF0C;&#x5219;anchor&#x7684;&#x4E2D;&#x5FC3;&#x4E3A; <script type="math/tex; ">\frac{i+0.5}{|f_{k}|},\frac{j+0.5}{|f_{k}|}</script>, <script type="math/tex; ">f_{k}</script>&#x662F;&#x7B2C;k&#x5C42;feature map&#x7684;size&#xFF08;&#x6BD4;&#x5982;38&#xFF09;</li>
<li>&#x7F29;&#x653E;&#x56E0;&#x5B50;:</li>
</ul>
<p><img src="https://upload-images.jianshu.io/upload_images/1828517-91ef6530e5dce4b2.png?imageMogr2/auto-orient/strip|imageView2/2/w/1240" alt="Scale"></p>
<p>&#x5176;&#x4E2D;<script type="math/tex; ">s_{min}</script>&#x4E3A;0.2&#xFF0C;<script type="math/tex; ">s_{max}</script>&#x4E3A;0.9&#xFF0C;m&#x4E3A;&#x6DFB;&#x52A0;&#x7684;feature map&#x7684;&#x5C42;&#x6570;&#xFF0C;&#x7F29;&#x653E;&#x56E0;&#x5B50;&#x5C31;&#x662F;&#x4E3A;&#x4E0D;&#x540C;feature map&#x9009;&#x62E9;&#x4E0D;&#x540C;&#x7684;&#x5927;&#x5C0F;&#x7684;anchor&#xFF0C;&#x8981;&#x6C42;&#x5C0F;&#x7684;feature map&#x5BF9;&#x5E94;&#x7684;anchor&#x5C3D;&#x91CF;&#x5927;&#xFF0C;&#x56E0;&#x4E3A;&#x8D8A;&#x5C0F;&#x7684;feature map&#xFF0C;&#x5176;feature map cell&#x7684;&#x611F;&#x53D7;&#x91CE;&#x5C31;&#x8D8A;&#x5927;</p>
</li>
<li><p>anchor&#x5BBD;&#x9AD8;&#xFF1A;</p>
<p><img src="https://upload-images.jianshu.io/upload_images/1828517-ba128e30ed7637e3.png?imageMogr2/auto-orient/strip|imageView2/2/w/1240" alt="width"></p>
<p><img src="https://upload-images.jianshu.io/upload_images/1828517-4898e977cc483570.png?imageMogr2/auto-orient/strip|imageView2/2/w/1240" alt="height"></p>
<p>&#x5176;&#x4E2D;&#xFF0C;<script type="math/tex; ">a_{r}∈\{1,2,3,1/2,1/3\}</script>&#xFF0C;&#x53EF;&#x4EE5;&#x7406;&#x89E3;&#x4E3A;&#x5728;&#x7F29;&#x653E;&#x56E0;&#x5B50;&#x9009;&#x62E9;&#x597D;anchor&#x5C3A;&#x5BF8;&#x540E;&#xFF0C;&#x7528;<script type="math/tex; ">a_{r}</script>&#x6765;&#x63A7;&#x5236;anchor&#x5F62;&#x72B6;&#xFF0C;&#x4ECE;&#x800C;&#x5F97;&#x5230;&#x591A;&#x5C3A;&#x5EA6;&#x7684;&#x5404;&#x79CD;anchor&#xFF0C;&#x5F53;<script type="math/tex; ">a_{r}=1</script>&#x65F6;&#xFF0C;&#x589E;&#x52A0;&#x4E00;&#x79CD;<script type="math/tex; ">s_{k}=sqrt(s_{k-1}s_{k+1})</script>&#xFF0C;&#x4E8E;&#x662F;&#x6BCF;&#x4E2A;feature map cell&#x901A;&#x5E38;&#x5BF9;&#x5E94;6&#x79CD;anchor&#x3002;</p>
</li>
</ul>
</li>
<li>&#x7F51;&#x7EDC;&#x7684;&#x8BAD;&#x7EC3;&#x76EE;&#x6807;&#x5C31;&#x662F;&#xFF0C;&#x56DE;&#x5F52;&#x5404;&#x4E2A;anchor&#x5BF9;&#x5E94;&#x7684;&#x7C7B;&#x522B;&#x548C;&#x4F4D;&#x7F6E;</li>
</ul>
<h2 id="training">Training</h2>
<h3 id="&#x6837;&#x672C;">&#x6837;&#x672C;</h3>
<ul>
<li>&#x6B63;&#x6837;&#x672C; &#x9009;&#x62E9;&#x4E0E;bounding box jaccard overlap&#xFF08;&#x4E24;&#x5F20;&#x56FE;&#x7684;&#x4EA4;&#x96C6;/&#x5E76;&#x96C6;&#xFF09;&#x5927;&#x4E8E;0.5&#x7684;anchor&#x4F5C;&#x4E3A;&#x6B63;&#x6837;&#x672C;</li>
<li>&#x6837;&#x672C;&#x6BD4;&#x4F8B; Hard negative mining&#xFF1A;&#x7531;&#x4E8E;&#x8D1F;&#x6837;&#x672C;&#x5F88;&#x591A;&#xFF0C;&#x9700;&#x8981;&#x53BB;&#x6389;&#x4E00;&#x90E8;&#x5206;&#x8D1F;&#x6837;&#x672C;&#xFF0C;&#x5148;&#x6574;&#x56FE;&#x7ECF;&#x8FC7;&#x7F51;&#x7EDC;&#xFF0C;&#x6839;&#x636E;&#x6BCF;&#x4E2A;anchor&#x7684;&#x6700;&#x9AD8;&#x7C7B;&#x7F6E;&#x4FE1;&#x5EA6;&#x8FDB;&#x884C;&#x6392;&#x5E8F;&#xFF0C;&#x9009;&#x62E9;&#x7F6E;&#x4FE1;&#x5EA6;&#x9760;&#x524D;&#x7684;&#x6837;&#x672C;&#xFF0C;&#x8FD9;&#x6837;&#x7B5B;&#x9009;&#x51FA;&#x6765;&#x7684;&#x8D1F;&#x6837;&#x672C;&#x4E5F;&#x4F1A;&#x66F4;&#x96BE;&#x8BC6;&#x522B;&#xFF0C;&#x5E76;&#x4E14;&#x6700;&#x7EC8;&#x6B63;&#x8D1F;&#x6837;&#x672C;&#x6BD4;&#x4F8B;&#x5927;&#x6982;&#x662F;1:3</li>
</ul>
<h3 id="loss">Loss</h3>
<p>&#x8FD8;&#x662F;&#x4E00;&#x5982;&#x65E2;&#x5F80;&#x7684;location loss + classification loss&#xFF0C;&#x5E76;&#x4E3A;location loss&#x6DFB;&#x52A0;&#x4E86;&#x7CFB;&#x6570;&#x3B1;&#xFF08;&#x7136;&#x800C;&#x5B9E;&#x9645;&#x4E0A;&#x3B1;=1&#xFF09;&#x8FDB;&#x884C;&#x5E73;&#x8861;&#xFF0C;&#x5E76;&#x5728;batch&#x7EF4;&#x5EA6;&#x8FDB;&#x884C;&#x5E73;&#x5747;</p>
<p><img src="ssd_1.png" alt=""></p>
<ul>
<li><script type="math/tex; ">x</script>&#x662F;<script type="math/tex; ">x_{ij}^{p}</script>&#x7684;&#x96C6;&#x5408;<script type="math/tex; ">x_{ij}^{p}={1,0}</script>&#xFF0C;&#x7528;&#x4E8E;&#x5224;&#x65AD;&#x7B2C;i&#x4E2A;anchor&#x662F;&#x5426;&#x662F;&#x7B2C;j&#x4E2A;bounding box&#x4E0A;&#x7684;p&#x7C7B;&#x6837;&#x672C;</li>
<li><script type="math/tex; ">c</script>&#x662F;<script type="math/tex; ">c_{i}^{p}</script>&#x7684;&#x96C6;&#x5408;&#xFF0C;<script type="math/tex; ">c_{i}^{p}</script>&#x662F;&#x7B2C;i&#x4E2A;anchor&#x9884;&#x6D4B;&#x4E3A;&#x7B2C;p&#x7C7B;&#x7684;&#x6982;&#x7387;</li>
<li>l&#x662F;&#x9884;&#x6D4B;&#x7684;bounding box&#x96C6;&#x5408;</li>
<li>g&#x662F;ground true bounding box&#x96C6;&#x5408;</li>
</ul>
<p>&#x5176;&#x4E2D;&#x5B9A;&#x4F4D;loss&#x4E0E;faster rcnn&#x76F8;&#x540C;</p>
<p><img src="ssd_2.png" alt="">
&#x8FD9;&#x4E2A;&#x5F0F;&#x5B50;&#x91CC;&#x7684;k&#x4E0D;&#x662F;&#x5F88;&#x660E;&#x786E;&#xFF0C;&#x5176;&#x5B9E;&#x60F3;&#x8868;&#x8FBE;&#x4E0D;&#x7B97;&#x80CC;&#x666F;0&#x7C7B;&#x7684;&#x610F;&#x601D;&#xFF0C;&#x4E14;&#x524D;&#x666F;&#x7C7B;&#x53EA;&#x4E3A;match&#x7684;&#x7C7B;&#x7B97;location loss</p>
<p>&#x5206;&#x7C7B;loss&#x5C31;&#x662F;&#x5F88;&#x5E38;&#x7528;&#x7684;softmax&#x4EA4;&#x53C9;&#x71B5;&#x4E86;</p>
<p><img src="ssd_3.png" alt=""></p>
<blockquote>
<p>&#x6838;&#x5FC3;&#x7684;&#x5185;&#x5BB9;&#x5230;&#x8FD9;&#x91CC;&#x5C31;&#x8BB2;&#x5B8C;&#x4E86;&#xFF0C;&#x5176;&#x5B9E;&#x8DDF;YOLO&#x548C;faster rcnn&#x4E5F;&#x5F88;&#x50CF;&#xFF0C;&#x662F;&#x4E00;&#x4E2A;&#x7528;anchor box&#x5145;&#x5F53;&#x56FA;&#x5B9A;&#x7684;proposal&#x7684;rpn&#xFF0C;&#x5E76;&#x4E14;&#x7528;&#x591A;&#x5C3A;&#x5EA6;&#x7684;anchor&#x6765;&#x9002;&#x5E94;&#x591A;&#x79CD;&#x5C3A;&#x5EA6;&#x548C;&#x5F62;&#x72B6;&#x7684;&#x76EE;&#x6807;&#x5BF9;&#x8C61;&#x3002;</p>
</blockquote>
<h2 id="detail">Detail</h2>
<p>&#x5728;&#x8BAD;&#x7EC3;&#x4E2D;&#x8FD8;&#x7528;&#x5230;&#x4E86;data augmentation&#xFF08;&#x6570;&#x636E;&#x589E;&#x5F3A;/&#x6269;&#x5145;&#xFF09;&#xFF0C;&#x6BCF;&#x5F20;&#x56FE;&#x7247;&#x591A;&#x662F;&#x7531;&#x4E0B;&#x5217;&#x4E09;&#x79CD;&#x65B9;&#x6CD5;&#x4E4B;&#x4E00;&#x968F;&#x673A;&#x91C7;&#x6837;&#x800C;&#x6765;</p>
<ul>
<li>&#x4F7F;&#x7528;&#x6574;&#x56FE;</li>
<li>crop&#x56FE;&#x7247;&#x4E0A;&#x7684;&#x4E00;&#x90E8;&#x5206;&#xFF0C;crop&#x51FA;&#x6765;&#x7684;min&#x9762;&#x79EF;&#x4E3A;0.1,0.3,0.5,0.7,0.9</li>
<li>&#x5B8C;&#x5168;&#x968F;&#x673A;&#x5730;crop</li>
</ul>
<p>&#x7136;&#x540E;&#x8FD9;&#x4E9B;&#x56FE;&#x7247;&#x4F1A;&#x88AB;resize&#x5230;&#x56FA;&#x5B9A;&#x7684;&#x5927;&#x5C0F;&#xFF0C;&#x968F;&#x673A;&#x6C34;&#x5E73;&#x7FFB;&#x8F6C;&#xFF0C;&#x52A0;&#x5165;&#x4E00;&#x4E9B;&#x56FE;&#x50CF;&#x4E0A;&#x7684;&#x566A;&#x58F0;&#xFF0C;&#x8BE6;&#x60C5;&#x53EF;&#x4EE5;&#x53C2;&#x8003;&#x53E6;&#x4E00;&#x7BC7;&#x8BBA;&#x6587;&#xFF1A;</p>
<p>Some improvements on deep convolutional neural network based image classification</p>
<p>&#x4ECE;&#x5207;&#x9664;&#x5B9E;&#x9A8C;&#x4E2D;&#xFF0C;&#x53EF;&#x4EE5;&#x770B;&#x5230;data augmentaion&#x662F;&#x5F88;&#x91CD;&#x8981;&#x7684;&#xFF08;&#x4ECE;65.6&#x5230;71.6&#xFF09;</p>
<p><img src="ssd_4.png" alt="">
&#x8FD9;&#x4E2A;&#x8868;&#x4E2D;&#x8FD8;&#x63D0;&#x5230;&#x4E86;atrous&#xFF0C;&#x5176;&#x5B9E;&#x662F;&#x6307;&#x7A7A;&#x6D1E;&#x5377;&#x79EF;&#xFF0C;&#x662F;&#x56FE;&#x50CF;&#x5206;&#x5272;&#xFF08;deeplab&#xFF09;&#x9886;&#x57DF;&#x9996;&#x5148;&#x63D0;&#x51FA;&#x7684;&#x4E00;&#x4E2A;&#x5377;&#x79EF;&#x5C42;&#x6539;&#x8FDB;&#xFF0C;&#x4E3B;&#x8981;&#x662F;&#x80FD;&#x8BA9;&#x6D4B;&#x8BD5;&#x901F;&#x5EA6;&#x66F4;&#x5FEB;&#x3002;&#x5177;&#x4F53;&#x53EF;&#x4EE5;&#x53C2;&#x8003; <a href="https://arxiv.org/pdf/1412.7062.pdf" target="_blank">ICLR2015 Deeplab</a></p>
<p>&#x4ECE;&#x8FD9;&#x4E2A;&#x8868;&#x4E2D;&#x4E5F;&#x53EF;&#x4EE5;&#x770B;&#x51FA;&#x591A;&#x79CD;&#x5F62;&#x72B6;&#x7684;anchor&#x53EF;&#x4EE5;&#x63D0;&#x5347;&#x51C6;&#x786E;&#x7387;</p>
<h2 id="result">Result</h2>
<p>&#x8F93;&#x5165;&#x5C3A;&#x5BF8;&#x4E3A;300x300&#xFF0C;batch size&#x4E3A;8&#x7684;SSD300&#x53EF;&#x4EE5;&#x505A;&#x5230;&#x5B9E;&#x65F6;(59FPS)&#x4E14;&#x51C6;&#x786E;(74.3% mAP)&#x7684;&#x6D4B;&#x8BD5;</p>
<h2 id="summary">Summary</h2>
<p>SSD&#x7B97;&#x662F;&#x4E00;&#x4E2A;&#x6539;&#x8FDB;&#x6027;&#x7684;&#x4E1C;&#x897F;&#xFF0C;&#x7AD9;&#x5728;Faster RCNN&#x7684;&#x80A9;&#x8180;&#x4E0A;&#x8FBE;&#x5230;&#x4E86;&#x5B9E;&#x65F6;&#x4E14;&#x51C6;&#x786E;&#x7684;&#x68C0;&#x6D4B;</p>

                                
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